A reaction norm framework for the evolution of learning: how cumulative experience shapes phenotypic plasticity.

Abstract

ABSTRACTLearning is a familiar process to most people, but it currently lacks a fully developed theoretical position within evolutionary biology. Learning (memory and forgetting) involves adjustments in behaviour in response to cumulative sequences of prior experiences or exposures to environmental cues. We therefore suggest that all forms of learning (and some similar biological phenomena in development, aging, acquired immunity and acclimation) can usefully be viewed as special cases of phenotypic plasticity, and formally modelled by expanding the concept of reaction norms to include additional environmental dimensions quantifying sequences of cumulative experience (learning) and the time delays between events (forgetting). Memory therefore represents just one of a number of different internal neurological, physiological, hormonal and anatomical ‘states’ that mediate the carry‐over effects of cumulative environmental experiences on phenotypes across different time periods. The mathematical and graphical conceptualisation of learning as plasticity within a reaction norm framework can easily accommodate a range of different ecological scenarios, closely linking statistical estimates with biological processes. Learning and non‐learning plasticity interact whenever cumulative prior experience causes a modification in the reaction norm (a) elevation [mean phenotype], (b) slope [responsiveness], (c) environmental estimate error [informational memory] and/or (d) phenotypic precision [skill acquisition]. Innovation and learning new contingencies in novel (laboratory) environments can also be accommodated within this approach. A common reaction norm approach should thus encourage productive cross‐fertilisation of ideas between traditional studies of learning and phenotypic plasticity. As an example, we model the evolution of plasticity with and without learning under different levels of environmental estimation error to show how learning works as a specific adaptation promoting phenotypic plasticity in temporally autocorrelated environments. Our reaction norm framework for learning and analogous biological processes provides a conceptual and mathematical structure aimed at usefully stimulating future theoretical and empirical investigations into the evolution of plasticity across a wider range of ecological contexts, while providing new interdisciplinary connections regarding learning mechanisms.